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Record W4252839646 · doi:10.2118/07-01-06

Understanding SAGD Producer Wellbore/Reservoir Damage Using Numerical Simulation

2007· article· en· W4252839646 on OpenAlexafffundabout
L. Zhao, Dennis B. Anderson, christopher O apos Rourke

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2007
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsHusky Energy (Canada)University of Alberta
FundersUniversity of Alberta
KeywordsInjectorWellboreComputer simulationPetroleum engineeringProduction rateProduction (economics)Oil productionPressure dropEnvironmental scienceOil fieldGeologyGeotechnical engineeringEngineeringMechanicsSimulationMechanical engineeringProcess engineering

Abstract

fetched live from OpenAlex

Abstract This paper reports on the results of a numerical simulation study of a SAGD well pair at Husky's Pikes Peak thermal project in the Lloydminster area. The pressure difference between the injector and producer wells gradually increased over a period of a year followed by a sudden and significant decline in the fluid production rate. Initial numerical simulation identified the problem as being due to damage near the production well, because only this damage pattern matched observations of field production and pressure. A detailed history match of the field data was then conducted. By adjusting the value of skin factor, excellent matches on the production rate and injection and production pressure were obtained. It was shown that by the time the skin factor had increased by a factor of 8 as a result of damage, and the pressure difference was about 900 kPa, the production rate started to drop significantly. Thereafter, the skin factor increased rapidly, and reached 30 times its original value. An acid treatment was performed on the production well. After the treatment, the skin factor returned to its original value, and the well pair returned to normal production. This case study is an example of how numerical simulation can be used as a tool to diagnose, identify and analyze SAGD operational problems. Introduction SAGD has become the leading technology for in situ recovery of heavy oil and bitumen in northern Alberta, Canada. The development of the technology has gone through several stages. The concept was first proposed by Butler(1) in the late 1970's. It was then tested at AOSTRA's Underground Test Facility (UTF) starting in the late 1980's. Phase A of the UTF test proved the concept in the field(2, 3). A number of issues were considered during the test: start up, sand control, steam trap control, reservoir heterogeneities, effect of solution gas and numerical simulation. The Phase A results were successfully scaled-up to longer wells in Phase B(4, 5). Horizontal drilling from the surface and operation of SAGD from the surface was the purpose of the Phase D study(6). This report summarizes the results of a SAGD field case study. At Husky's Pikes Peak thermal project, located in the Lloydminster area, one SAGD well pair experienced a sudden decline in fluid production. A high pressure drop between the injector and producer wells identified the problem as being due to damage near the production well. Simulation revealed the pattern and extent of the damage. After an acid treatment of the production well, production returned to normal. Brief Description of the Field Project The Pikes Peak thermal project started in 1981 using cyclic steam stimulation (CSS) technology. The project was located in the Lloydminster area on the Saskatchewan side, as shown in Figure 1. Later on, SAGD technology was also used. The details of the project history and the area geology can be found in earlier publications(7, 8). In the subject area, the depth from the surface to the top of the pay zone was around 500 m.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.510
Threshold uncertainty score0.726

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.065
GPT teacher head0.290
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2007
Admission routes3
Has abstractyes

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